Modeling Protein Aggregation Kinetics: The Method of Second Stochasticization.

Area of Science:

• Biophysics
• Computational Biology
• Cell Biology

Background:

• Protein aggregate nucleation and growth are vital for cell organelle structure and disease pathogenesis.
• Stochastic nature and diversity of aggregates challenge current theoretical and computational models.
• Kinetic Monte Carlo simulations are powerful but computationally intensive for complex aggregation systems.

Purpose of the Study:

• To introduce a novel, efficient computational approach for modeling stochastic protein aggregation kinetics.
• To accurately account for fluctuations in nucleation, growth, dissociation, and fragmentation of aggregates.
• To bridge the gap between in vivo cell biology and detailed computational modeling.

Main Methods:

• Developed "second stochasticization," a method introducing noise into statistically averaged moment equations.
• Applied stochastic moment equations to model diverse species and molecularities in aggregation.
• Simulated aggregation dynamics considering primary/secondary nucleation, elongation, dissociation, and fragmentation.

Main Results:

• The "second stochasticization" method efficiently models stochastic aggregation, especially for modest fluctuations typical in vivo.
• Simulations revealed a scaling law linking aggregate size/number fluctuations to total monomer count.
• This scaling law was validated against experimental data.

Conclusions:

• "Second stochasticization" offers an efficient alternative to computationally demanding methods like Gillespie algorithm for aggregation modeling.
• The identified scaling law provides new insights into the statistical behavior of protein aggregates.
• This method holds promise for advancing our understanding of in vivo aggregation processes and related diseases.